https://davidohnstad.net/enterprise-ai-success-metrics-definition-gap/ AI & Machine Learning in Enterprise Software | Product Management Mon, 02 Nov 2026 18:54:46 +0000 hourly 1 https://wordpress.org/?v=7.1.4 https://davidohnstad.net/enterprise-ai-success-metrics-definition-gap/#comment-1020 Mon, 02 Nov 2026 18:54:46 +0000 https://davidohnstad.net/?p=151#comment-1020 […] The fix isn’t better collaboration between ML and product teams—it’s changing the organizational structure so that AI capabilities are developed inside product squads, not handed down from a centralized ML team. That requires product managers who can write SQL, understand data pipelines, and collaborate directly with data scientists. It also requires data scientists who are willing to iterate on model design based on user feedback, not just optimize for algorithmic performance. This is where AI & Machine Learning in Enterprise Software becomes a product management skill, not just a technical capability. The best AI features David has shipped were built by cross-functional squads where the PM, designer, engineer, and data scientist sat together and co-designed the feature from the decision point backward to the model architecture. For more on this, see why most AI projects fail. […]

]]> https://davidohnstad.net/enterprise-ai-success-metrics-definition-gap/#comment-993 Mon, 26 Oct 2026 15:26:08 +0000 https://davidohnstad.net/?p=151#comment-993 […] Governance isn’t bureaucracy. It’s the structural answer to three questions: Who decides when the model is wrong? Who’s notified when performance degrades? What’s the rollback plan when predictions break a downstream process? If those questions don’t have answers before deployment, you’re not shipping a product—you’re handing off a liability. For more on this, see why most enterprise AI projects fail. […]

]]> https://davidohnstad.net/enterprise-ai-success-metrics-definition-gap/#comment-958 Mon, 12 Oct 2026 18:03:33 +0000 https://davidohnstad.net/?p=151#comment-958 […] Gate 2: Uncertainty Tolerance. Does the business process tolerate probabilistic outputs, or does it require deterministic results with full auditability? Vendor risk decisions are binary: approved, rejected, or escalated. Procurement teams don’t act on a 73% risk score — they need a yes/no recommendation with a documented rationale. AI models produce confidence intervals, not certainties. That’s valuable in fraud detection, where you’re scoring thousands of transactions and investigating the top 2% by risk score. It’s friction in vendor onboarding, where every decision requires a human sign-off anyway. If your workflow already has a human-in-the-loop gate that reviews 100% of outputs, AI isn’t removing the bottleneck — it’s adding a pre-processing step that obscures the decision logic. Rule-based systems produce auditable decision trees: “Vendor rejected because SOC 2 report expired 90+ days ago.” AI systems produce: “Vendor scored 0.68 on risk index due to weighted feature importances across 140 input variables.” The second explanation doesn’t survive a compliance audit. For more on this, see why most AI projects fail. […]

]]> https://davidohnstad.net/enterprise-ai-success-metrics-definition-gap/#comment-818 Mon, 24 Aug 2026 09:14:46 +0000 https://davidohnstad.net/?p=151#comment-818 […] Before deploying any autonomous agent, organizations need to pass a four-layer readiness audit. This is not a maturity model—it’s a binary assessment. If you can’t answer “yes” to every question in a layer, you are not ready for agents at that scope. The Agent Readiness Stack works from the foundation up: data integrity, process documentation, feedback infrastructure, and human escalation protocols. Most organizations fail at layer one and never check the other three. For more on this, see why most AI projects fail. […]

]]> https://davidohnstad.net/enterprise-ai-success-metrics-definition-gap/#comment-815 Mon, 24 Aug 2026 09:12:49 +0000 https://davidohnstad.net/?p=151#comment-815 […] The dollars tell the story. A Fortune 500 client of David Ohnstad’s spent $4.1 million on a customer intelligence AI platform in 2023. Eighteen months later, three teams had production use cases: two were generating reports that could have been built faster with SQL, and one was running sentiment analysis that the customer success team didn’t trust and stopped referencing in their quarterly reviews. The platform wasn’t wrong — it was solving problems nobody had prioritized solving. For more on this, see Why most enterprise AI projects fail. […]

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